• Title/Summary/Keyword: Pixel clustering

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A Study on the Classification for Satellite Images using Hybrid Method (하이브리드 분류기법을 이용한 위성영상의 분류에 관한 연구)

  • Jeon, Young-Joon;Kim, Jin-Il
    • The KIPS Transactions:PartB
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    • v.11B no.2
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    • pp.159-168
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    • 2004
  • This paper presents hybrid classification method to improve the performance of satellite images classification by combining Bayesian maximum likelihood classifier, ISODATA clustering and fuzzy C-Means algorithm. In this paper, the training data of each class were generated by separating the spectral signature using ISODATA clustering. We can classify according to pixel's membership grade followed by cluster center of fuzzy C-Means algorithm as the mean value of training data for each class. Bayesian maximum likelihood classifier is performed with prior probability by result of fuzzy C-Means classification. The results shows that proposed method could improve performance of classification method and also perform classification with no concern about spectral signature of the training data. The proposed method Is applied to a Landsat TM satellite image for the verifying test.

A Image Contrast Enhancement by Clustering of Image Histogram (영상의 히스토그램 군집화에 의한 영상 대비 향상)

  • Hong, Seok-Keun;Lee, Ki-Hwan;Cho, Seok-Je
    • Journal of the Institute of Convergence Signal Processing
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    • v.10 no.4
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    • pp.239-244
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    • 2009
  • Image contrast enhancement has an important role in image processing applications. Conventional contrast enhancement techniques, histogram stretching and histogram equalization, and many methods based on histogram equalization often fail to produce satisfactory results for broad variety of low-contrast images. So, this paper proposes a new image contrast enhancement method based on the clustering method. The number of cluster of histogram is found by analysing the histogram of original image. The histogram components is classified using K-means algorithm. And then these histogram components are performed histogram stretching and histogram equalization selectively by comparing cluster range with pixel rate of cluster. From the expremental results, the proposed method was more effective than conventional contrast enhancement techniques.

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Infrared Image Segmentation by Extracting and Merging Region of Interest (관심영역 추출과 통합에 의한 적외선 영상 분할)

  • Yeom, Seokwon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.26 no.6
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    • pp.493-497
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    • 2016
  • Infrared (IR) imaging is capable of detecting targets that are not visible at night, thus it has been widely used for the security and defense system. However, the quality of the IR image is often degraded by low resolution and noise corruption. This paper addresses target segmentation with the IR image. Multiple regions of interest (ROI) are extracted by the multi-level segmentation and targets are segmented from the individual ROI. Each level of the multi-level segmentation is composed of a k-means clustering algorithm an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering algorithm initializes the parameters of the Gaussian mixture model (GMM) and the EM algorithm iteratively estimates those parameters. Each pixel is assigned to one of clusters during the decision. This paper proposes the selection and the merging of the extracted ROIs. ROI regions are selectively merged in order to include the overlapped ROI windows. In the experiments, the proposed method is tested on an IR image capturing two pedestrians at night. The performance is compared with conventional methods showing that the proposed method outperforms others.

Chaotic Features for Traffic Video Classification

  • Wang, Yong;Hu, Shiqiang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.8 no.8
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    • pp.2833-2850
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    • 2014
  • This paper proposes a novel framework for traffic video classification based on chaotic features. First, each pixel intensity series in the video is modeled as a time series. Second, the chaos theory is employed to generate chaotic features. Each video is then represented by a feature vector matrix. Third, the mean shift clustering algorithm is used to cluster the feature vectors. Finally, the earth mover's distance (EMD) is employed to obtain a distance matrix by comparing the similarity based on the segmentation results. The distance matrix is transformed into a matching matrix, which is evaluated in the classification task. Experimental results show good traffic video classification performance, with robustness to environmental conditions, such as occlusions and variable lighting.

Efficient Superpixel Generation Method Based on Image Complexity

  • Park, Sanghyun
    • Journal of Multimedia Information System
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    • v.7 no.3
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    • pp.197-204
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    • 2020
  • Superpixel methods are widely used in the preprocessing stage as a method to reduce computational complexity by simplifying images while maintaining the characteristics of the images in the computer vision applications. It is common to generate superpixels of similar size and shape based on the pixel values rather than considering the characteristics of the image. In this paper, we propose a method to control the sizes and shapes of generated superpixels, considering the contents of an image. The proposed method consists of two steps. The first step is to over-segment an image so that the boundary information of the image is well preserved. In the second step, generated superpixels are merged based on similarity to produce the target number of superpixels, where the shapes of superpixels are controlled by limiting the maximum size and the proposed roundness metric. Experimental results show that the proposed method preserves the boundaries of the objects in an image more accurately than the existing method.

Optimizing Speed For Adaptive Local Thresholding Algorithm U sing Dynamic Programing

  • Due Duong Anh;Hong Du Tran Le;Duan Tran Duc
    • Proceedings of the IEEK Conference
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    • summer
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    • pp.438-441
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    • 2004
  • Image binarization using a global threshold value [3] performs at high speed, but usually results in undesired binary images when the source images are of poor quality. In such cases, adaptive local thresholding algorithms [1][2][3] are used to obtain better results, and the algorithm proposed by A.E.Savekis which chooses local threshold using fore­ground and background clustering [1] is one of the best thresholding algorithms. However, this algorithm runs slowly due to its re-computing threshold value of each central pixel in a local window MxM. In this paper, we present a dynamic programming approach for the step of calculating local threshold value that reduces many redundant computations and improves the execution speed significantly. Experiments show that our proposal improvement runs more ten times faster than the original algorithm.

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Fast hierarchical image segmentation based on mathematical morphology (수리형태론에 기반한 고속 계층적 영상분할)

  • 김해룡;홍원학;김남철
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.33B no.10
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    • pp.38-49
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    • 1996
  • In this paper, we propose a fast hierarchical image segmentation using mathematical morphology. The proposed segmentation method is composed of five basic steps; multi-thresholding, open-close by reconstructing, mode operation, marker extraction, and region decision. In the multi-thresholding, an input image is simplified by Lloyd clustering algorithm. The multi-thresholded image then is more simplified by open-close by reconstruction and mode operating. In the region decision, to which region each uncertainty pixel belongs finally is decided by a watershed algorithm. Experimental results show that the quality of the segmentation results by the proposed method is not inferior to that by the conventional method and the average times elapsed by the proposed method can be reduced by one tghird of those elapsed by the conventional method.

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Hole Filling Technique for Depth Map using Color Image Pixel Clustering (컬러 영상 화소 분류를 이용한 깊이 영상의 홀을 채우는 기법)

  • Lee, Geon-Won;Han, Jong-Ki
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2020.07a
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    • pp.55-57
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    • 2020
  • 실감미디어의 수요가 높아짐에 따라, 실감 미디어 컨텐츠 제작에 반드시 필요한 깊이영상에 대한 중요성이 커지고 있다. 다시점 영상으로부터 계산된 깊이 영상은 물체 주위와 배경 영역에 홀을 가지고 있다. 이러한 깊이영상에서의 홀을 채울 때, 이에 대응하는 컬러영상의 색상 특성을 고려하는 방법을 제안한다. 본 논문에서는 컬러 영상의 화소들을 색상 유사성을 이용하여 클래스로 분류하고, 홀의 깊이정보를 예측할 때 같은 클래스의 유효한 깊이값 만을 사용하는 방법을 소개한다. 제안하는 방법을 사용하면 깊이영상의 홀을 효율적으로 채워 넣을 수 있다. 실감미디어 제작에 있어 제안하는 방법을 사용한다면, 사실감 있는 깊이 정보를 얻을 수 있다.

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Context-Dependent Classification of Multi-Echo MRI Using Bayes Compound Decision Model (Bayes의 복합 의사결정모델을 이용한 다중에코 자기공명영상의 context-dependent 분류)

  • 전준철;권수일
    • Investigative Magnetic Resonance Imaging
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    • v.3 no.2
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    • pp.179-187
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    • 1999
  • Purpose : This paper introduces a computationally inexpensive context-dependent classification of multi-echo MRI with Bayes compound decision model. In order to produce accurate region segmentation especially in homogeneous area and along boundaries of the regions, we propose a classification method that uses contextual information of local enighborhood system in the image. Material and Methods : The performance of the context free classifier over a statistically heterogeneous image can be improved if the local stationary regions in the image are disassociated from each other through the mechanism of the interaction parameters defined at he local neighborhood level. In order to improve the classification accuracy, we use the contextual information which resolves ambiguities in the class assignment of a pattern based on the labels of the neighboring patterns in classifying the image. Since the data immediately surrounding a given pixel is intimately associated with this given pixel., then if the true nature of the surrounding pixel is known this can be used to extract the true nature of the given pixel. The proposed context-dependent compound decision model uses the compound Bayes decision rule with the contextual information. As for the contextual information in the model, the directional transition probabilities estimated from the local neighborhood system are used for the interaction parameters. Results : The context-dependent classification paradigm with compound Bayesian model for multi-echo MR images is developed. Compared to context free classification which does not consider contextual information, context-dependent classifier show improved classification results especially in homogeneous and along boundaries of regions since contextual information is used during the classification. Conclusion : We introduce a new paradigm to classify multi-echo MRI using clustering analysis and Bayesian compound decision model to improve the classification results.

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Vegetation Cover Type Mapping Over The Korean Peninsula Using Multitemporal AVHRR Data (시계열(時系列) AVHRR 위성자료(衛星資料)를 이용한 한반도 식생분포(植生分布) 구분(區分))

  • Lee, Kyu-Sung
    • Journal of Korean Society of Forest Science
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    • v.83 no.4
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    • pp.441-449
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    • 1994
  • The two reflective channels(red and near infrared spectrum) of advanced very high resolution radiometer(AVHRR) data were used to classify primary vegetation cover types in the Korean Peninsula. From the NOAA-11 satellite data archive of 1991, 27 daytime scenes of relatively minimum cloud coverage were obtained. After the initial radiometric calibration, normalized difference vegetation index(NDVI) was calculated for each of the 27 data sets. Four or five daily NDVI data were then overlaid for each of the six months starting from February to November and the maximum value of NDVI was retained for every pixel location to make a monthly composite. The six bands of monthly NDVI composite were nearly cloud free and used for the computer classification of vegetation cover. Based on the temporal signatures of different vegetation cover types, which were generated by an unsupervised block clustering algorithm, every pixel was classified into one of the six cover type categories. The classification result was evaluated by both qualitative interpretation and quantitative comparison with existing forest statistics. Considering frequent data acquisition, low data cost and volume, and large area coverage, it is believed that AVHRR data are effective for vegetation cover type mapping at regional scale.

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